F. Hutter, H. H. Hoos, and K. Leyton-brown, Automated Configuration of Mixed Integer Programming Solvers, Proc. CPAIOR-10, pp.186-202, 2010.
DOI : 10.1007/978-3-642-13520-0_23

A. Eiben, Z. Michalewicz, M. Schoenauer, and J. Smith, Parameter Control in Evolutionary Algorithms: Parameter Setting in Evolutionary Algorithms, of Studies Comp. Intel, pp.19-46, 2007.
DOI : 10.1007/978-3-662-05094-1_8
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.8979

Y. Zhi, M. A. De-oca, T. Stützle, and M. Birattari, Modern continuous optimization algorithms for tuning real and integer algorithm parameters, Proc. ANTS'7, pp.203-214, 2010.

M. Birattari, Z. Yuan, P. Balaprakash, and T. Stützle, Automated algorithm tuning using F-Races: Recent developments, Proc. MIC'09, 2009.

J. Dubois-lacoste, M. López-ibáñez, and T. Stützle, Automatic configuration of state-of-the-art multi-objective optimizers using the TP+PLS framework, Proceedings of the 13th annual conference on Genetic and evolutionary computation, GECCO '11, pp.2019-2026, 2011.
DOI : 10.1145/2001576.2001847

T. Bartz-beielstein, C. Lasarczyk, and M. Preuss, Sequential Parameter Optimization, 2005 IEEE Congress on Evolutionary Computation, pp.773-780, 2005.
DOI : 10.1109/CEC.2005.1554761

V. Nannen and A. Eiben, Efficient relevance estimation and value calibration of evolutionary algorithm parameters, 2007 IEEE Congress on Evolutionary Computation, pp.975-980, 2007.
DOI : 10.1109/CEC.2007.4424460

F. Hutter, H. Hoos, K. Leyton-brown, and T. Stützle, ParamILS: an automatic algorithm configuration framework, J. Artif. Intel. Research, vol.36, issue.1, pp.267-306, 2009.

E. Zitzler, L. Thiele, M. Laumanns, C. Fonseca, and V. Da-fonseca, Performance assessment of multiobjective optimizers: an analysis and review, IEEE Transactions on Evolutionary Computation, vol.7, issue.2, pp.117-132, 2003.
DOI : 10.1109/TEVC.2003.810758

M. Ghallab, D. Nau, and P. Traverso, Automated Planning, Theory and Practice, 2004.

J. Biba¨?biba¨?, P. Savéant, M. Schoenauer, and V. Vidal, An Evolutionary Metaheuristic Based on State Decomposition for Domain-Independent Satisficing Planning, Proc. 20 th ICAPS, pp.18-25, 2010.

K. Miettinen, Nonlinear multiobjective optimization, 1999.
DOI : 10.1007/978-1-4615-5563-6

Y. Jin, T. Okabe, and B. Sendhoff, Adapting Weighted Aggregation for Multiobjective Evolution Strategies, Proc. EMO 2001, pp.96-110, 1993.
DOI : 10.1007/3-540-44719-9_7
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.17.5797

M. Schoenauer, P. Savéant, and V. Vidal, Divide-and-Evolve: A New Memetic Scheme for Domain-Independent Temporal Planning, Proc. 6 th EvoCOP, pp.247-260, 2006.
DOI : 10.1007/11730095_21
URL : https://hal.archives-ouvertes.fr/inria-00000975

M. R. Khouadjia, M. Schoenauer, V. Vidal, J. Dréo, and P. Savéant, Multi-objective AI Planning: Evaluating DaE YAHSP on a Tunable Benchmark, Proc. EMO'2013, 2013.
DOI : 10.1007/978-3-642-37140-0_7
URL : https://hal.archives-ouvertes.fr/hal-00750560

M. Do and S. Kambhampati, SAPA: A Multi-Objective Metric Temporal Planner, J. Artif. Intell. Res. (JAIR), vol.20, pp.155-194, 2003.

I. Refanidis and I. Vlahavas, Multiobjective heuristic state-space planning, Artificial Intelligence, vol.145, issue.1-2, pp.1-32, 2003.
DOI : 10.1016/S0004-3702(02)00371-5
URL : http://doi.org/10.1016/s0004-3702(02)00371-5

A. Gerevini, A. Saetti, and I. Serina, An approach to efficient planning with numerical fluents and multi-criteria plan quality, Artificial Intelligence, vol.172, issue.8-9, pp.8-9, 2008.
DOI : 10.1016/j.artint.2008.01.002

A. Gerevini and D. Long, Preferences and Soft Constraints in PDDL3, ICAPS Workshop on Planning with Preferences and Soft Constraints, pp.46-53, 2006.

Y. Chen, B. Wah, and C. Hsu, Temporal Planning using Subgoal Partitioning and Resolution in SGPlan, J. of Artificial Intelligence Research, vol.26, issue.1, pp.323-369, 2006.

S. Edelkamp and P. Kissmann, Optimal Symbolic Planning with Action Costs and Preferences, Proc. 21 st IJCAI, pp.1690-1695, 2009.

R. Fikes and N. Nilsson, Strips: A new approach to the application of theorem proving to problem solving, Artificial Intelligence, vol.2, issue.3-4, pp.27-120, 1971.
DOI : 10.1016/0004-3702(71)90010-5

J. Biba¨?biba¨?, P. Savéant, M. Schoenauer, and V. Vidal, On the Benefit of Sub-Optimality within the Divide-and-Evolve Scheme, Proc. 10 th EvoCOP, pp.23-34, 2010.

P. Haslum and H. Geffner, Admissible Heuristics for Optimal Planning, Proc. AIPS-2000, pp.70-82, 2000.

V. Vidal, A Lookahead Strategy for Heuristic Search Planning, Proceedings of the 14 th ICAPS, pp.150-159, 2004.

H. Lourenço, O. Martin, and T. Stützle, Iterated Local Search, Handbook of metaheuristics, pp.320-353, 2003.
DOI : 10.1007/0-306-48056-5_11

E. Zitzler and S. Künzli, Indicator-Based Selection in Multiobjective Search, Proc. PPSN VIII, pp.832-842, 2004.
DOI : 10.1007/978-3-540-30217-9_84

J. Biba¨?biba¨?, P. Savéant, M. Schoenauer, and V. Vidal, On the Generality of Parameter Tuning in Evolutionary Planning, Proc 12 th GECCO, pp.241-248, 2010.